gum::Estimator< GUM_SCALAR > Class Template Reference

#include <estimator.h>

Collaboration diagram for gum::Estimator< GUM_SCALAR >:

Public Member Functions
	Estimator ()
	Default constructor.
	Estimator (const IBayesNet< GUM_SCALAR > *bn)
	Constructor with Bayesian network.
	~Estimator ()
GUM_SCALAR	confidence ()
	computes the maximum length of confidence interval for each possible value of each variable
void	update (Instantiation I, GUM_SCALAR w)
	updates the estimator with a given sample
const Tensor< GUM_SCALAR > &	posterior (const DiscreteVariable &var)
	returns the posterior of a node
void	clear ()
	refresh the estimator state as empty
void	setFromBN (const IBayesNet< GUM_SCALAR > *bn, const NodeSet &hardEvidence)
	estimator initializing
void	setFromLBP (LoopyBeliefPropagation< GUM_SCALAR > *lbp, const NodeSet &hardEvidence, GUM_SCALAR virtualLBPSize)
	sets the estimatoor object with posteriors obtained by LoopyBeliefPropagation

Private Member Functions
GUM_SCALAR	EV (std::string name, Idx val)
	returns expected value of Bernouilli variable (called by it's name) of given parameter
GUM_SCALAR	variance (std::string name, Idx val)
	returns variance of Bernouilli variable (called by it's name) of given parameter

Private Attributes
HashTable< std::string, std::vector< GUM_SCALAR > >	estimator_
	estimator represented by hashtable between each variable name and a vector of cumulative sample weights
GUM_SCALAR	wtotal_
	cumulated weights of all samples
Size	ntotal_
	number of generated samples
const IBayesNet< GUM_SCALAR > *	bn_
	Bayesian network on which approximation is done.
HashTable< std::string, Tensor< GUM_SCALAR > * >	_target_posteriors_
	the set of single posteriors computed during the last inference

Detailed Description

template<typename GUM_SCALAR>
class gum::Estimator< GUM_SCALAR >

Definition at line 58 of file estimator.h.

Constructor & Destructor Documentation

Estimator() [1/2]

template<typename GUM_SCALAR>

Estimator::Estimator

(

)

Default constructor.

Definition at line 53 of file estimator_tpl.h.

                                     {
    GUM_CONSTRUCTOR(Estimator);
    wtotal_ = (GUM_SCALAR)0.;
    ntotal_ = (Size)0;
    bn_     = nullptr;
  }

References Estimator(), bn_, ntotal_, and wtotal_.

Referenced by Estimator(), Estimator(), and ~Estimator().

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Estimator() [2/2]

template<typename GUM_SCALAR>

Estimator::Estimator ( const IBayesNet< GUM_SCALAR > * bn )

explicit

Constructor with Bayesian network.

Definition at line 61 of file estimator_tpl.h.

                                                                      : Estimator() {
    bn_ = bn;
 
    for (gum::NodeGraphPartIterator iter = bn->nodes().begin(); iter != bn->nodes().end(); ++iter)
      estimator_.insert(bn->variable(*iter).name(),
                        std::vector< GUM_SCALAR >(bn->variable(*iter).domainSize(), 0.0));
 
    GUM_CONSTRUCTOR(Estimator);
  }

References Estimator(), gum::NodeGraphPart::begin(), bn_, gum::DiscreteVariable::domainSize(), gum::NodeGraphPart::end(), estimator_, gum::Variable::name(), gum::DAGmodel::nodes(), and gum::IBayesNet< GUM_SCALAR >::variable().

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~Estimator()

template<typename GUM_SCALAR>

INLINE Estimator::~Estimator

(

)

Definition at line 72 of file estimator_tpl.h.

                                             {
    GUM_DESTRUCTOR(Estimator);
    // remove all the posteriors computed
    clear();
  }

References Estimator(), and clear().

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Member Function Documentation

clear()

template<typename GUM_SCALAR>

void Estimator::clear

(

)

refresh the estimator state as empty

this function remove all the statistics in order to restart the computations.

Definition at line 188 of file estimator_tpl.h.

                                      {
    estimator_.clear();
    wtotal_ = (GUM_SCALAR)0;
    ntotal_ = Size(0);
    for (const auto& pot: _target_posteriors_)
      delete pot.second;
    _target_posteriors_.clear();
  }

References _target_posteriors_, estimator_, ntotal_, and wtotal_.

Referenced by ~Estimator().

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confidence()

template<typename GUM_SCALAR>

GUM_SCALAR Estimator::confidence

(

)

computes the maximum length of confidence interval for each possible value of each variable

Returns

maximum length of confidence interval

Definition at line 174 of file estimator_tpl.h.

                                                 {
    GUM_SCALAR ic_max = 0;
 
    for (auto iter = estimator_.begin(); iter != estimator_.end(); ++iter) {
      for (Idx i = 0; i < iter.val().size(); i++) {
        GUM_SCALAR ic = GUM_SCALAR(2 * 1.96 * std::sqrt(variance(iter.key(), i) / (ntotal_ - 1)));
        if (ic > ic_max) ic_max = ic;
      }
    }
 
    return ic_max;
  }

References estimator_, ntotal_, and variance().

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EV()

template<typename GUM_SCALAR>

GUM_SCALAR Estimator::EV ( std::string name, Idx val )

private

returns expected value of Bernouilli variable (called by it's name) of given parameter

Returns

Expected value of Bernouilli variable (called by it's name) of given parameter

Parameters

name	variable's name, considered as a Bernouilli variable
val	the parameter of the Bernouilli variable

computes the amount of cumulative weights for paramater val over the amount of total cumulative weights

Definition at line 158 of file estimator_tpl.h.

                                                                {
    return estimator_[name][val] / wtotal_;
  }

References estimator_, and wtotal_.

Referenced by variance().

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posterior()

template<typename GUM_SCALAR>

const Tensor< GUM_SCALAR > & Estimator::posterior

(

const DiscreteVariable &

var

)

returns the posterior of a node

Returns

a constant ref to the posterior probability of the variable node

Parameters

var	the variable node which we want posterior for

returns the vector of cumulated weight bias for each value of the variable normalized as a CPT

Exceptions

NotFound

if variable node is not in estimator.

Definition at line 136 of file estimator_tpl.h.

                                                                                            {
    Tensor< GUM_SCALAR >* p = nullptr;
 
    if (!estimator_.exists(var.name())) GUM_ERROR(NotFound, "Target variable not found")
 
    // check if we have already computed the posterior
    if (_target_posteriors_.exists(var.name())) {
      p = _target_posteriors_[var.name()];
    } else {
      p = new Tensor< GUM_SCALAR >();
      *p << var;
      _target_posteriors_.insert(var.name(), p);
    }
 
    p->fillWith(estimator_[var.name()]);
    p->normalize();
    return *p;
  }

References _target_posteriors_, estimator_, GUM_ERROR, and gum::Variable::name().

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setFromBN()

template<typename GUM_SCALAR>

void Estimator::setFromBN	(	const IBayesNet< GUM_SCALAR > *	bn,
		const NodeSet &	hardEvidence )

estimator initializing

sets the estimator object with 0-filled vectors corresponding to each non evidence node

Definition at line 81 of file estimator_tpl.h.

                                                                                       {
    for (gum::NodeGraphPartIterator iter = bn->nodes().begin(); iter != bn->nodes().end(); ++iter) {
      auto v = bn->variable(*iter).name();
 
      if (!hardEvidence.contains(*iter)) {
        if (estimator_.exists(v))
          estimator_[v]
              = std::vector< GUM_SCALAR >(bn->variable(*iter).domainSize(), (GUM_SCALAR)0.0);
        else
          estimator_.insert(
              v,
              std::vector< GUM_SCALAR >(bn->variable(*iter).domainSize(), (GUM_SCALAR)0.0));
      }
    }
  }

References gum::NodeGraphPart::begin(), gum::Set< Key >::contains(), gum::DiscreteVariable::domainSize(), gum::NodeGraphPart::end(), estimator_, gum::Variable::name(), gum::DAGmodel::nodes(), and gum::IBayesNet< GUM_SCALAR >::variable().

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setFromLBP()

template<typename GUM_SCALAR>

void Estimator::setFromLBP	(	LoopyBeliefPropagation< GUM_SCALAR > *	lbp,
		const NodeSet &	hardEvidence,
		GUM_SCALAR	virtualLBPSize )

sets the estimatoor object with posteriors obtained by LoopyBeliefPropagation

Definition at line 101 of file estimator_tpl.h.

                                                                                                 {
    for (const auto& node: lbp->BN().nodes()) {
      if (!hardEvidence.contains(node)) {
        std::vector< GUM_SCALAR > v;
        auto                      p = lbp->posterior(node);
        gum::Instantiation        inst(p);
 
        for (inst.setFirst(); !inst.end(); ++inst) {
          v.push_back(p[inst] * virtualLBPSize);
        }
 
        estimator_.insert(lbp->BN().variable(node).name(), v);
      }
    }
    ntotal_ = (Size)virtualLBPSize;
    wtotal_ = virtualLBPSize;
  }

References gum::BayesNetInference< GUM_SCALAR >::BN(), gum::Set< Key >::contains(), gum::Instantiation::end(), estimator_, ntotal_, gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), gum::Instantiation::setFirst(), and wtotal_.

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update()

template<typename GUM_SCALAR>

void Estimator::update	(	Instantiation	I,
		GUM_SCALAR	w )

updates the estimator with a given sample

Parameters

I	the sample used to update the estimators
weight	bias for the given sample

adds the sample weight to each node's given value in the estimator

Definition at line 124 of file estimator_tpl.h.

                                                                    {
    wtotal_ += w;
    ntotal_ += (Size)1;
 
    for (Idx i = 0; i < I.nbrDim(); i++) {
      if (estimator_.exists(I.variable(i).name())) estimator_[I.variable(i).name()][I.val(i)] += w;
    }
  }

References estimator_, gum::Variable::name(), gum::Instantiation::nbrDim(), ntotal_, gum::Instantiation::val(), gum::Instantiation::variable(), and wtotal_.

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variance()

template<typename GUM_SCALAR>

GUM_SCALAR Estimator::variance ( std::string name, Idx val )

private

returns variance of Bernouilli variable (called by it's name) of given parameter

Returns

variance of Bernouilli variable (called by it's name) of given parameter

Parameters

name	variable's name, considered as a Bernouilli variable
val	the parameter of the Bernouilli variable

computes variance for Bernouilli law using EV(name, val)

Definition at line 165 of file estimator_tpl.h.

                                                                      {
    GUM_SCALAR p = EV(name, val);
    return p * (1 - p);
  }

References EV().

Referenced by confidence().

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Member Data Documentation

_target_posteriors_

template<typename GUM_SCALAR>

HashTable< std::string, Tensor< GUM_SCALAR >* > gum::Estimator< GUM_SCALAR >::_target_posteriors_

private

the set of single posteriors computed during the last inference

the posteriors are owned by LazyPropagation.

Definition at line 176 of file estimator.h.

Referenced by clear(), and posterior().

bn_

template<typename GUM_SCALAR>

const IBayesNet< GUM_SCALAR >* gum::Estimator< GUM_SCALAR >::bn_

private

Bayesian network on which approximation is done.

Definition at line 146 of file estimator.h.

Referenced by Estimator(), and Estimator().

estimator_

template<typename GUM_SCALAR>

HashTable< std::string, std::vector< GUM_SCALAR > > gum::Estimator< GUM_SCALAR >::estimator_

private

estimator represented by hashtable between each variable name and a vector of cumulative sample weights

Definition at line 137 of file estimator.h.

Referenced by Estimator(), clear(), confidence(), EV(), posterior(), setFromBN(), setFromLBP(), and update().

ntotal_

template<typename GUM_SCALAR>

Size gum::Estimator< GUM_SCALAR >::ntotal_

private

number of generated samples

Definition at line 143 of file estimator.h.

Referenced by Estimator(), clear(), confidence(), setFromLBP(), and update().

wtotal_

template<typename GUM_SCALAR>

GUM_SCALAR gum::Estimator< GUM_SCALAR >::wtotal_

private

cumulated weights of all samples

Definition at line 140 of file estimator.h.

Referenced by Estimator(), clear(), EV(), setFromLBP(), and update().

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The documentation for this class was generated from the following files:

• agrum/BN/inference/tools/estimator.h
• agrum/BN/inference/tools/estimator_tpl.h